{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "05b71777-adca-4a75-8c60-b8b33efbc036",
   "metadata": {},
   "source": [
    "Chapter 7\n",
    "# 移动相关性系数\n",
    "Book_6《数据有道》 | 鸢尾花书：从加减乘除到机器学习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eb8f031c-5f66-4fac-a931-d764bcb06f99",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: yfinance in c:\\users\\james\\anaconda3\\lib\\site-packages (0.2.31)\n",
      "Requirement already satisfied: pytz>=2022.5 in c:\\users\\james\\anaconda3\\lib\\site-packages (from yfinance) (2022.7)\n",
      "Requirement already satisfied: numpy>=1.16.5 in c:\\users\\james\\anaconda3\\lib\\site-packages (from yfinance) (1.23.5)\n",
      "Requirement already satisfied: html5lib>=1.1 in c:\\users\\james\\anaconda3\\lib\\site-packages (from yfinance) (1.1)\n",
      "Requirement already satisfied: lxml>=4.9.1 in c:\\users\\james\\anaconda3\\lib\\site-packages (from yfinance) (4.9.1)\n",
      "Requirement already satisfied: frozendict>=2.3.4 in c:\\users\\james\\anaconda3\\lib\\site-packages (from yfinance) (2.3.8)\n",
      "Requirement already satisfied: requests>=2.31 in c:\\users\\james\\anaconda3\\lib\\site-packages (from yfinance) (2.31.0)\n",
      "Requirement already satisfied: beautifulsoup4>=4.11.1 in c:\\users\\james\\anaconda3\\lib\\site-packages (from yfinance) (4.11.1)\n",
      "Requirement already satisfied: appdirs>=1.4.4 in c:\\users\\james\\anaconda3\\lib\\site-packages (from yfinance) (1.4.4)\n",
      "Requirement already satisfied: peewee>=3.16.2 in c:\\users\\james\\anaconda3\\lib\\site-packages (from yfinance) (3.17.0)\n",
      "Requirement already satisfied: multitasking>=0.0.7 in c:\\users\\james\\anaconda3\\lib\\site-packages (from yfinance) (0.0.11)\n",
      "Requirement already satisfied: pandas>=1.3.0 in c:\\users\\james\\anaconda3\\lib\\site-packages (from yfinance) (1.5.3)\n",
      "Requirement already satisfied: soupsieve>1.2 in c:\\users\\james\\anaconda3\\lib\\site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.3.2.post1)\n",
      "Requirement already satisfied: six>=1.9 in c:\\users\\james\\anaconda3\\lib\\site-packages (from html5lib>=1.1->yfinance) (1.16.0)\n",
      "Requirement already satisfied: webencodings in c:\\users\\james\\anaconda3\\lib\\site-packages (from html5lib>=1.1->yfinance) (0.5.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\james\\anaconda3\\lib\\site-packages (from pandas>=1.3.0->yfinance) (2.8.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\james\\anaconda3\\lib\\site-packages (from requests>=2.31->yfinance) (3.4)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\james\\anaconda3\\lib\\site-packages (from requests>=2.31->yfinance) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\james\\anaconda3\\lib\\site-packages (from requests>=2.31->yfinance) (2023.5.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\james\\anaconda3\\lib\\site-packages (from requests>=2.31->yfinance) (1.26.14)\n"
     ]
    }
   ],
   "source": [
    "# !pip install yfinance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1654f1a6-b7b6-46c5-9661-18bd018db996",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initializations and download results \n",
    "import pandas as pd\n",
    "import pandas_datareader as web\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "# import pandas_datareader as pdr\n",
    "import yfinance as yf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "87c0f9c6-4f72-4b2e-98cc-9a420bac80b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  2 of 2 completed\n"
     ]
    }
   ],
   "source": [
    "tickers = ['^GSPC','TSLA']\n",
    "\n",
    "from datetime import datetime\n",
    "startdate = datetime(2015,8,1)\n",
    "enddate = datetime(2021,8,1)\n",
    "data = yf.download(tickers = tickers,\n",
    "                  start = startdate,\n",
    "                  end = enddate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5cbf813a-765f-43fc-a8e1-0351697eb3de",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_pickle('data.pkl')\n",
    "# data_ = pd.read_pickle('data.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6fb6300-33e2-4ac7-a9ed-5dc8553ede53",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c14def26-00e0-48c5-baf1-05e6321271a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TSLA</th>\n",
       "      <th>^GSPC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-03</th>\n",
       "      <td>17.332666</td>\n",
       "      <td>2098.040039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-04</th>\n",
       "      <td>17.752001</td>\n",
       "      <td>2093.320068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-05</th>\n",
       "      <td>18.008667</td>\n",
       "      <td>2099.840088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-06</th>\n",
       "      <td>16.408667</td>\n",
       "      <td>2083.560059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-07</th>\n",
       "      <td>16.167334</td>\n",
       "      <td>2077.570068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-26</th>\n",
       "      <td>219.206665</td>\n",
       "      <td>4422.299805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-27</th>\n",
       "      <td>214.926666</td>\n",
       "      <td>4401.459961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-28</th>\n",
       "      <td>215.660004</td>\n",
       "      <td>4400.640137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-29</th>\n",
       "      <td>225.783340</td>\n",
       "      <td>4419.149902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-30</th>\n",
       "      <td>229.066666</td>\n",
       "      <td>4395.259766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1510 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  TSLA        ^GSPC\n",
       "Date                               \n",
       "2015-08-03   17.332666  2098.040039\n",
       "2015-08-04   17.752001  2093.320068\n",
       "2015-08-05   18.008667  2099.840088\n",
       "2015-08-06   16.408667  2083.560059\n",
       "2015-08-07   16.167334  2077.570068\n",
       "...                ...          ...\n",
       "2021-07-26  219.206665  4422.299805\n",
       "2021-07-27  214.926666  4401.459961\n",
       "2021-07-28  215.660004  4400.640137\n",
       "2021-07-29  225.783340  4419.149902\n",
       "2021-07-30  229.066666  4395.259766\n",
       "\n",
       "[1510 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data['Adj Close']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d939d443-a3fd-4a9a-99b3-f0f3133717d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% daily log return\n",
    "\n",
    "daily_log_r = df.apply(lambda x: np.log(x) - np.log(x.shift(1)))\n",
    "daily_log_r = daily_log_r.dropna()\n",
    "\n",
    "df_corr = daily_log_r['^GSPC'].rolling(100).corr(daily_log_r['TSLA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9ab5a99e-435b-45a5-bc2a-c4b9c28e85d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Rolling correlation')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# daily return of selected date range\n",
    "ax.plot(df_corr[df_corr.first_valid_index():df_corr.index[-1]])\n",
    "ax.axhline(y=0.5, color='r', linestyle='-')\n",
    "ax.set_ylabel('Rolling correlation')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
